Cost Volume Construction
The SWL transforms reference view feature maps into the target view domain at the individual hypothesis plane, and then a total of \(M+1\)  feature volumes are generated.
The variance-based cost volume formation method from MVSNet [1]} is used for the fusion of these feature volumes into a compact one.
Moreover, we adopt a cascade design from Gu et al. [2]} to further improve the final depth quality.
Specifically, at level \(l(l>1)\) , \(d_{min}\)  and \(d_{max}\)  is recalculated based on the prediction of level \(l-1\) , then the new depth range and the new number of planes \(D_l\)  is used to determine the new intervals. Depth hypothesis for level \(l\)  is then updated using equ (REF ).
The corresponding displacements are calculated via the same spherical coordinate mapping procedure.
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